Advanced Energy Management System for Generator–Battery Hybrid Power System in Ships: A Novel Approach with Optimal Control Algorithms

Abstract

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery’s state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship’s operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships.

1. Introduction

Various research projects on enhancing the energy efficiency of ship systems and reducing greenhouse gas emissions are being conducted [1,2,3]. In addition to advancements in propulsion systems, a significant amount of research has focused on improving the power system efficiency, including the use of onboard hybrid power sources [4,5,6]. Integrating photovoltaic generation, fuel cells, or renewable energy into conventional power systems has been shown to reduce greenhouse gas emissions and air pollutants, while offering cost savings compared with the use of traditional gas turbines and internal combustion engines [7,8]. However, these systems have high initial installation costs and technological limitations [5,9]. Hybrid systems combining diesel generators with battery systems have been introduced to enhance the efficiency of conventional electric propulsion systems [10,11]. Supercapacitors and variable-speed engine generators have demonstrated improvements in energy efficiency and a reduction in pollutant emissions [12,13,14]. Additionally, integrating a solar hybrid power system into a built-in energy storage system and a conventional diesel generator has been shown to reduce fuel consumption and carbon dioxide emissions [15,16]. A hybrid system that includes molten carbonate fuel cells (MCFCs), batteries, and diesel generators has been found to be effective at reducing carbon dioxide emissions compared with conventional diesel generators [17]. An efficient power management system is essential for integrating multiple energy sources and storage systems into a microgrid with renewable energy and storage devices [18]. Consequently, several control techniques and power management strategies have been developed, including a new power distribution control strategy based on logical thresholds [19,20]. The application of optimal power management methods to the electric power system of an electric propulsion vessel has been shown to minimize the operating costs and meet greenhouse gas (GHG) emission limit criteria [21,22].

In hybrid electric propulsion systems that incorporate various power generation sources, the design of the controller is crucial for the efficient operation and control of these sources based on load demands [23,24]. In addition, integrating the controller with an energy management system along with the existing power management system is essential [25,26,27]. One study focused on a hybrid electric vessel powered by a dual proton exchange membrane fuel cell (PEMFC), a battery, and an ultracapacitor (UC). In this study, an equivalent consumption minimization strategy (ECMS) was employed to optimize the total power output of a PEMFC [9,27]. A map search engine was used to maximize the efficiency and the power distribution to the battery, and the UC was managed based on the state of charge (SOC) of each component. Simulations demonstrated a high efficiency for this configuration [28].

Simulations of a hybrid system combining liquefied natural gas (LNG) and batteries on a tugboat, integrated with an energy management system using rule-based control, demonstrated that CO₂ emissions and daily fuel costs could be reduced when compared to a system without such a control strategy [29].

An analysis of a hybrid power system containing a fuel cell was conducted using an adaptive equivalent consumption minimization strategy (A-ECMS) and state-based and fuzzy-logic-based EMS (Energy Management System). The results indicate that the A-ECMS strategy can maintain a system efficiency of above 60% under most operating conditions and significantly reduce the fluctuations in the output power of the fuel cell [30].

A distributed variable sag slope control strategy was implemented to enhance the SOC equalization of vessels equipped with a fuel cell (FC) as the energy source and batteries, and supercapacitors as the energy storage system (ESS). This approach improved the speed and accuracy of the SOC equalization, optimized the characteristics of different energy storage devices, and reduced the degradation of these devices [31].

In a battery hybrid power system, a rule-based control method utilizing an ECMS is used to train a neural network. Simulations confirmed the accuracy of this method, demonstrating that the stability of the system was maintained by effectively controlling the speed, voltage, and current of the propulsion motor under varying battery SOC levels and rapidly changing ship loads [32].

A simulation of the battery hybrid method was conducted to compare and analyze its performance with those of conventional diesel-powered ships in terms of carbon dioxide emissions. The results demonstrate that the battery hybrid method effectively reduces carbon dioxide emissions compared with the use of a controller that implements load-sharing optimal control. Additionally, a life cycle assessment (LCA) confirmed that the proposed system is environmentally friendly throughout the energy generation process [33].

An energy management framework based on model predictive control (MPC) was developed for a ship’s hybrid power generation system with a battery system by incorporating an advanced shipboard energy management strategy (EMS). Simulations that had the aim of ensuring safe voyages and a long battery life while considering battery capacity and SOC values showed that the proposed framework could achieve a 3.5% reduction in energy consumption [34].

A hybrid optimization algorithm combining chaotic algorithms and gray wolf optimization (GWO) was used to design an energy management strategy with non-linear model predictive control (NMPC). The study found that NMPC based on the GWO algorithm could reduce fuel consumption by approximately 26% and carbon emissions by approximately 56%, compared with other algorithms [35].

The operation of a ship’s DC microgrid model, consisting of equipment, a controller, and a communication network, was simulated and the results with and without a secondary control strategy were compared. The results demonstrate that the secondary control strategy effectively addressed the problem of unbalanced SOC in the energy-storage module. The SOC of the battery gradually converges under the secondary strategy, proving that it is both reasonable and efficient [36].

A study was conducted on the electrification of low-tonnage vessels operating on short cycles with high-power demands; a situation which presents significant technical challenges. The study involved the real test case of the Seine River ferry with an installed propulsion power of 330 kW. The supercapacitor and battery-based hybrid structures were compared with those of a conventional propulsion system. The results showed that the hybrid structures achieved reductions in the CO₂ emissions of 18% and 29.7%, respectively, compared with the conventional method, corresponding to reductions of approximately 382 and 626 t of CO₂ over 20 years of operation [37].

For the optimal energy management system of the generator non-equivalent parallel running operation system proposed in this study, a load-sharing algorithm was designed based on the generator’s optimal efficiency operation. This approach aims to improve upon previous methods that only partially enhance the efficiency. Using MATLAB/Simulink, we modeled an actual operational ship by linking the energy storage system to an existing power system to maximize the energy efficiency. An algorithm was developed to operate at the optimal efficiency point based on fuel consumption characteristics relative to the power output of the diesel generator. The goal of the control algorithm designed to operate at this optimal point is to maximize the energy efficiency of the ship. The proposed control method is compared with existing methods to verify its effectiveness, with the goal of reducing fuel consumption and greenhouse gas (GHG) emissions.

2. Methodology

The ship’s specifications and system requirements were selected to assess the effectiveness of the proposed generator non-equivalent parallel running operation system (NEQP). The optimal operating point of the engine was determined based on the design of the control rules and operation modes. These were subsequently applied to the load profile of an actual ship, and the proposed NEQP method was compared with a conventional control method using MATLAB(R2021b)/Simulink. The optimal efficiency criteria for the generator were established to design rules for minimizing fuel consumption. An optimal operating point efficiency of 85% was selected considering the SFOC (Specific Fuel Oil Consumption) of the target ship and the stability of the generator engine. For the battery, charging and discharging limits were set to ensure safety and a long battery life. In this configuration, the generator provides a constant power output at the optimal efficiency point to satisfy the load requirements of the ship, and charges the battery whenever excess power is available. If the power output of the generator is insufficient for handling the system load, the controller is designed to release the energy stored in the battery to support part of the load.

Step 1. Selection of the system specifications.

The generator–battery hybrid system was evaluated using a vehicle carrier as the target ship and a 1500 kW battery system. The characteristics of, and basic information on, the diesel generators of the target ship are summarized in

Table 1. Specifications of diesel generator on the vessel.

Maker	HYUNDAI-HiMSEN
Type	7H21/32
Engine power	1415 [kW]
Generator power	1330 [kW]
Engine speed	900 [rpm]
Engine set	3 [sets]

, and these data were used for the modeling. The target ship was equipped with three identical 1330 kW diesel generators produced by HYUNDAI-HiMSEN (Manufacturer: Hyundai Heavy Industries, Ulsan, Korea).

The Specific Fuel Oil Consumption (SFOC) required to calculate the fuel consumption based on the diesel generator’s load is summarized in

Table 2. Specific fuel oil consumption value of diesel generator.

Engine power [%]	10	25	50	75	100
SFOC [g/kWh]	322.3	240.8	203.3	196.7	192.2

. The data were derived from the manufacturer’s factory test operation report provided when the target ship was built.

As illustrated in Figure 1, operating the generator at low loads results in a higher fuel oil consumption than operating it at relatively high loads. This indicates that the fuel efficiency varies with the load, with a noticeable difference between low- and high-load operations. For loads above 50%, there was little variation in the efficiency. However, for loads below 50%, the efficiency decreased sharply, as indicated by the steep slope. A simulation model was constructed considering these characteristics.

The optimal operating point of the engine, based on the lowest Specific Fuel Oil Consumption (SFOC) at 100% load, poses a risk of blackout due to the additional load when the generator runs at full capacity. To ensure safety, the Power Management System (PMS) activates a standby generator when the load exceeds 85%, reducing the strain on the main generator. Therefore, this paper designates 85% load as the point of highest efficiency with safety assured.

Step 2. Design of the energy management system.

To use the controller, the generator load-sharing criteria were set according to the system load size and battery SOC, which was designed by setting the rules for the energy-optimal control criteria for the optimal operation mode of the NEQP as an energy management system. As shown in Equation (1), the load is defined as the sum of the power output of the power generation source and the SOC of the battery. The command states of the generator output are divided into ${DG}_{stop}$, ${DG}_{min}$, ${DG}_{opt}$, and ${DG}_{max}$.

The optimal operating point (${DG}_{opt}$) for efficient operation was selected, and the load of the system and the power balance of each generator and battery were equal to the sum of the power outputs of the three generators and the capacity of the battery with respect to the load of the ship.

$$W_{load}=W_{DG1}+W_{DG2}+W_{DG3}+W_{batt}$$

(1)

• $W_{load}$: Required load [kW] on electrical power system
• $W_{DG1}$: Output [kW] of No. 1 diesel generator
• $W_{DG2}$: Output [kW] of No. 2 diesel generator
• $W_{DG3}$: Output [kW] of No. 3 diesel generator
• $W_{batt}$: Output [kW] of battery
• ${DG}_{stop}$: 0 [%]
• ${DG}_{min}$: 50 [%]
• ${DG}_{opt}$: 85 [%]
• ${DG}_{max}$: 100 [%]

The control rules were designed to ensure that each generator operates at its optimal efficiency point for each mode. The optimal energy control rules for minimizing the energy consumption while adhering to the control logic criteria based on the ship’s load and battery SOC conditions are outlined in

Table 3. Proposed NEQP optimal efficiency algorithm for the generator parallel operation system with battery.

SOC [%]		Power	Mode	${\mathit{W}}_{\mathit{D}\mathit{G}1}$	${\mathit{W}}_{\mathit{D}\mathit{G}2}$	${\mathit{W}}_{\mathit{D}\mathit{G}3}$	${\mathit{W}}_{\mathit{b}\mathit{a}\mathit{t}\mathit{t}}$
	Load Condition
0 ~ 30	$0<W_{load}$$\le {DG}_{opt}$		M1	${DG}_{opt}$	${DG}_{stop}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$
	${DG}_{opt}$$<W_{load}$$\le {DG}_{opt}$ × 2		M2	${DG}_{opt}$	${DG}_{opt}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$
	${DG}_{opt}$$\times 2<W_{load}$$\le {DG}_{opt}$ × 3		M3	${DG}_{opt}$	${DG}_{opt}$	${DG}_{opt}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$$-W_{DG3}$
	${DG}_{opt}$$\times 3<W_{load}$$\le {DG}_{max}$ × 3		Mmax	${DG}_{max}$	${DG}_{max}$	${DG}_{max}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$$-W_{DG3}$
30 ~ 80	$0<W_{load}$$\le {DG}_{min}$		M1	${DG}_{opt}$	${DG}_{stop}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$
	${DG}_{min}$$<W_{load}$$\le {DG}_{opt}$$+{DG}_{min}$		M1	${DG}_{opt}$	${DG}_{stop}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$
	${DG}_{opt}$$+{DG}_{min}$$<W_{load}$$\le ({DG}_{opt}$$\times 2)+{DG}_{min}$		M2	${DG}_{opt}$	${DG}_{opt}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$
	$({DG}_{opt}$$\times 2)+{DG}_{min}$$<W_{load}$$\le {DG}_{max}$ × 3		M3	${DG}_{opt}$	${DG}_{opt}$	${DG}_{opt}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$$-W_{DG3}$
80 ~ 100	$0<W_{load}$$\le {DG}_{opt}$		Mvar	$W_{load}$	${DG}_{stop}$	${DG}_{stop}$	0
	${DG}_{opt}$$<W_{load}$$\le {DG}_{opt}$ × 2		M1	${DG}_{opt}$	${DG}_{stop}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$
	${DG}_{opt}$$\times 2<W_{load}$$\le {DG}_{opt}$ × 3		M2	${DG}_{opt}$	${DG}_{opt}$	${DG}_{stop}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$
	${DG}_{opt}$$\times 3<W_{load}$$\le {DG}_{max}$ × 3		M3	${DG}_{opt}$	${DG}_{opt}$	${DG}_{opt}$	$W_{load}$$-W_{DG1}$$-W_{DG2}$$-W_{DG3}$

$W_{load}$: Required load [kW] on electrical power system, $W_{DG1}$: output [kW] of No. 1 diesel generator, $W_{DG2}$: output [kW] of No. 2 diesel generator, $W_{DG3}$: output [kW] of No. 3 diesel generator, $W_{batt}$: output [kW] of battery, ${DG}_1$: No. 1 diesel generator, ${DG}_2$: No. 2 diesel generator, ${DG}_3$: No. 3 diesel generator, ${DG}_{stop}$: 0 [%], ${DG}_{min}$: 50 [%], ${DG}_{opt}$: 85 [%], ${DG}_{max}$: 100 [%], M1~3: Mode 1~3, Mvar: variable mode, Max: max mode.

. As indicated in the table, the generators operate in the optimal mode in 10 of the 12 operating modes. The rules for each load zone were as follows: the battery is charged only when the SOC is below 30% and discharged only when the SOC is above 80%. When the SOC is between 30% and 80%, the battery is in a normal state, and charging/discharging rules are applied based on the system load. In this zone, all generators operate at their optimal efficiency. When the battery SOC is 80% or higher, charging is not allowed, and if the power load required by the ship is less than the generator’s optimal output, the fuel consumption per unit horsepower will increase owing to the low-load operation.

Figure 2 shows the operating states of the power generator and battery based on the control rules.

Figure 3 presents a flowchart of the control rules based on the operational mode and control rules. Using the ship load and battery SOC as input values, the power output command of the generator was determined according to these control rules. Commands M1, M2, M3, Mmax, and Mvar vary based on the SOC of the battery, and the power output command of the generator is adjusted according to the current operating load.

Step 3. Selection of the load profile

To verify the reliability of the diesel generator–battery hybrid system model with the optimal efficiency algorithm-based control method, several scenarios were constructed using actual operational data from the target ship, and simulations were conducted. As shown in Figure 4, the sailing route of the target ship was as follows: after docking at the port of call for approximately 19.2 h, the ship departed and sailed for about 72 h. It then entered the next port and docked for 62.4 h for cargo loading and unloading, before departing again and sailing for 60 h. As depicted in Figure 5, the system load records from the actual ship, covering approximately 222 h (800,000 s) of operation, were extracted and converted into load profiles for constructing the simulation scenarios. The fuel consumption was then calculated using MATLAB/Simulink software (R2021b).

The sections labeled “In Port” or “Sailing” include the loads required for sailing, operating equipment during cargo loading and unloading, and maintaining the ship’s living quarters. These are the basic load sections at which a ship can operate normally. The three high-load sections labeled “Arrival” or “Departure” represent periods of maximum power consumption due to large loads such as bow thrusters when entering or leaving a port. Data were collected and stored in 10 s intervals during the voyage, with each 10 s interval assuming that the values retained the previous data.

Step 4. Simulation

The simulations were performed to verify the reliability of the diesel generator–battery hybrid system model with the optimal efficiency control method applied to the existing power system using MATLAB/Simulink. The controller inputs, which vary in real time, include the total load of the ship and the battery state of charge (SOC). The output was the power output command of the generator engine. As shown in Figure 6, the system was configured by integrating the battery system with the existing power system of the target ship, which consisted of three engine generators. Simulations were conducted using symmetric and asymmetric load-sharing methods, and the results were compared. The system load was managed using a MATLAB/Simulink block that facilitates real-time load variations.

As depicted in Figure 7, the generator operation characteristics of the target ship were applied to the model to extract data, such as the generator’s power output, fuel consumption, voltage, and current relative to the load. The model included three generators with a battery capacity set greater than that of the generators. The initial SOC value [%] was set using a block. A scenario simulating the power load required for the actual operation of the target ship was input as the load. Additionally, a data collection system was modeled to extract and store the load data over time according to the scenario. A controller implementing both symmetric and asymmetric control methods was added to compare the proposed NEQP control method with existing methods.

3. Results

Based on the selected load profile, the simulation results for systems with symmetric load sharing, asymmetric load sharing, and the proposed NEQP controller were compared, as shown in Figure 8. In the symmetric load-sharing method, the system operated with medium-to-low loads in the 50–60% range, with power outputs equally distributed between the two generators, resulting in a lower efficiency. No. 3 DG was activated to handle peak loads during port arrivals and departures, demonstrating that the load was shared through parallel operation. Adding a sufficiently large battery to this system and connecting it to the grid can mitigate efficiency losses by using the stored energy for temporary peak and partial loads, thereby reducing the need for frequent parallel operations.

When operating with an asymmetric load-sharing controller, No. 1 DG functions at its optimal operating point of 85% (1130.5 kW), whereas the remaining load is managed by No. 2 and 3 in parallel operation. No. 2 DG operates at its lowest efficiency, handling a load of approximately 0–35% (0–400 kW) of its optimal capacity. No. 3 DG is activated to address the peak loads that arise during port arrivals and departures, with the load shared through a parallel operation. Consistently operating one generator at a high efficiency is advantageous for improving the overall efficiency and reducing fuel consumption. However, the remaining load is handled by another generator operating at a lower efficiency, and all changing system loads are handled in the low-load state. Thus, while a longer load scenario duration and a larger efficiency operating point based on load sharing can be beneficial, the system efficiency decreases as the operating point deviates from the optimal point compared with symmetric load operation. To enhance the efficiency of this system, a sufficiently large battery could be added and connected to the grid. In this scenario, the energy stored in the battery can be used for low and temporary peak loads handled by No. 2 DG, thereby compensating for the reduced efficiency.

The simulation of a generator–battery hybrid system, in which an additional battery is connected to the existing power system, demonstrates that the generators operate at their optimal points depending on the system load and battery SOC, whereas the battery assists with loads followed by charging and discharging. No. 1 DG operates at its optimal point of 85% (1130.5 kW), handling most of the power system load. The remaining load is managed through a parallel operation with Nos. 2 and 3, and some of the load is handled by the battery. All three generators operated at their optimal efficiency points. When No. 1 DG does not operate at its optimal point, the system, as shown in the battery SOC graph, is controlled to protect the battery when the SOC reaches 80%. In these cases, the generator operates at a variable load rather than at its optimal point, and the battery stops charging. During these periods, the system load is managed by a single generator, and the power output fluctuates according to the load changes. No. 3 DG is activated to handle peak loads during port arrivals and departures, with the load being shared through a parallel operation. The simulation results indicate that the generators operate at their optimal efficiency points in all zones, except those set for battery protection based on the battery SOC. The generators operate in parallel according to the load size, and the battery charges and discharges smoothly. When the generator operates at its optimal point, the charged battery discharges to manage the additional load above the optimal efficiency point. In the two zones in which the battery SOC reached the upper limit of 80% (1200 kWh), the battery stopped charging and discharged slightly but steadily, as indicated by the power input and output. When the SOC reached the lower limit of 30% (450 kWh), the battery immediately started charging again.

Table 4. Total fuel oil consumption (unit: [ton]).

	DG No.	No. 1	No. 2	No. 3	Grand Total
Load Sharing Method
Symmetric		32.17	21.25	0.07	53.49
Asymmetric		47.49	4.44	0.06	51.99
NEQP		47.89	2.55	0.06	50.5

provides details of the fuel oil consumption of each generator and the combined fuel oil consumption of all three generators throughout the simulation. At the end of the approximately 222-h simulation, the cumulative fuel consumption for the symmetric load-sharing controller was 53.49 t. The fuel consumption distribution was approximately 60% for generator 1, 40% for No. 2 DG, and approximately 0.1% for No. 3 DG, which handled the peak load. In total, 53.49 t of fuel oil were consumed.

For the asymmetric load-sharing controller, the total fuel consumption was 51.99 t. No. 1 DG operated at its optimal efficiency point and handled most of the system load, while No. 2 DG operated at a low efficiency and load and managed the remaining load. The cumulative fuel consumption of No. 1 DG was 47.49 t, which represented approximately 91.3% of the total cumulative fuel consumption. The cumulative fuel consumption of No. 2 DG was 4.44 t, accounting for approximately 8.5%, while that of No. 3 DG was approximately 0.1%. The total fuel oil consumption in the asymmetric load-sharing control mode was 51.99 t. The cumulative fuel consumption of No. 1 DG, which constitutes approximately 91.3% of the total fuel consumption, represents a significant reduction compared with that of the symmetric load-sharing control mode. This efficiency improvement results in fuel savings of approximately 1.5 t. By segmenting the system load into specific zones, one or more generators can operate at their optimal efficiency points, thereby reducing the fuel oil consumption and pollutant emissions. However, the remaining generators often operate at very low efficiencies in low-load zones, which can increase fuel consumption and pollutant emissions. These results may vary depending on the load profile and the operating time.

The final cumulative fuel consumption of the system with the proposed NEQP controller was 50.5 t. All generators operated at their optimal efficiency points and operated in conjunction with a battery to manage the power system load. No. 1 DG handled most of the power system load with a cumulative fuel consumption of 47.89 t, accounting for approximately 83% of the total fuel consumption. The cumulative fuel consumption of No. 2 DG was 2.55 t, or approximately 5%, while that of No. 3 DG was minimal at approximately 0.12%. Because all generators operated at their optimal efficiency points, the total fuel consumption was reduced compared with previous simulations. Despite No. 1 DG having the highest cumulative fuel consumption of 47.89 t, which is approximately 83% of the total, the overall fuel consumption was lower because Nos. 2 and 3 DG also operated efficiently, and the battery was connected to the grid to handle part of the load.

4. Discussion

4.1. Analysis of Results through Comparison

Figure 9 illustrates the fuel oil consumption of the generator–battery hybrid system with the proposed NEQP optimal efficiency control method compared with those of the conventional symmetric and asymmetric load-sharing control methods. The results demonstrate that the fuel consumption is the lowest with the proposed NEQP optimal efficiency algorithm.

Table 5. Comparison of total fuel oil consumption for the load sharing method.

Load Sharing Method		Symmetric	Asymmetric
	Total FOC [ton]	53.49	51.99
NEQP	50.5	2.99 [ton] (5.59 [%])	1.49 [ton] (2.87 [%])

presents a comparison of the total fuel consumption for each control method based on the simulation results. The symmetric load-sharing method resulted in a total fuel consumption of 53.49 t, which is about 2.99 t higher than the 50.5 t consumed using the proposed NEQP control method. This indicates that the proposed method achieves a higher efficiency than conventional methods.

While adding a battery to the existing power system and applying NEQP is the most effective solution, the cost of adding the battery must be considered. In contrast, implementing symmetric and asymmetric load sharing methods in the existing power system may only require changes to the control logic, with no need for additional equipment installations.

4.2. Economic Benefits of Applying NEQP

Additionally, maintaining generators at their optimal operating points simplifies the prediction of maintenance cycles and is expected to yield significant economic benefits through reduced fuel and maintenance costs.

Table 6. Analysis of fuel savings and economic benefits per year depending on fuel type.

	Type of Fuel
	High-Sulfur Fuel Oil (3.5 [%])	Low-Sulfur Fuel Oil (0.5 [%])	LNG
USD per ton [USD]	520	602	708
Savings of fuel per year [ton]	115.96	115.96	115.96
Savings of USD per year [USD]	60,299.2	69,807.9	82,099.7
Savings of KRW per year [10 K KRW]	8333.35	9647.45	11,346.18

shows that if a ship using low-sulfur oil completes approximately 38 voyages per year, the NEQP optimal efficiency control method could save 115.96 t of fuel oil. This translates to an expected cost saving of approximately USD 69,807 or approximately KRW 96.47 million [38].

This reduction translates to expected fuel savings of approximately 115.96 t for 38 voyages per year, equating to cost savings of approximately USD 69,807. The savings are anticipated to be even greater when using LNG, and the economic benefits are expected to increase if marine fuel oil prices rise in the future or if the supply price of carbon-free fuel exceeds that of LNG.

4.3. Limitations and Future Research

• This study has limitations due to the use of simulated data based on the actual load profile of a ship’s voyage. Therefore, it is essential to conduct further research by installing and validating the proposed control methods on software and equipment in actual operational ships.
• Further research is needed to explore different system designs to determine if the C-rate of the battery system can handle peak loads during port arrival and departure, and if the ship’s operational capability can be maintained without reserving auxiliary power for rapidly changing onboard loads. The use of dual-battery systems, supercapacitors, and advanced control methods should also be investigated to ensure the safety of the power systems under peak loads.
• This paper acknowledges certain limitations, such as not considering the battery’s price, weight, and safety in the event of a fire. Future research should address these issues through a comprehensive review of both economic and safety implications.
• The study also focuses on long time-scale simulations, meaning it does not include components that capture transient states, such as Automatic Voltage Regulators (AVR). Future developments will need to address these transient states, presenting a new challenge for model enhancement.
• The reduction in fuel consumption can lead to verified reductions in CO2 emissions, which presents another potential avenue for expanded research.

5. Conclusions

To improve the efficiency of the existing power system, three control methods were compared and analyzed. A model was developed using MATLAB/Simulink, and a simulation was conducted based on 222 h of real ship operation data.

1.

When the symmetric load-sharing control method was applied, the number of generators operating was determined based on power loads of 85%, 170%, and 255%. It was observed that the load was evenly distributed among the generators operating in parallel. During the simulation, a total of 53.49 tons of fuel was consumed.

2.

With the asymmetric load-sharing control method, the number of generators was also determined based on the size of the power load. However, in parallel operation, one generator operated at its optimal efficiency point, with the remaining load distributed among the other generators. This resulted in a total fuel consumption of 51.99 tons during the simulation.

3.

When the NEQP control method proposed in this study was applied, the battery was charged or discharged depending on the defined load range and the battery’s state of charge (SOC). This enabled the generators to maintain optimal efficiency for a longer period compared to the previously mentioned control methods. As a result, the total fuel consumption was reduced to 50.5 tons, the lowest among the control methods.

4.

The simulation results demonstrated a reduction in generator fuel consumption of approximately 2.99 tons, or 5.59%, over 222 h of operation.